Yesterday we looked at Billy Penn’s graphics about the cooler stations and I mentioned a few ways the graphic could be improved. So last night I created a graphic where I explored the limited scope of the data, but also showing how low the temperatures were, relative to the air temperature outside, using weather data from the National Weather Service, admittedly from Philadelphia International Airport, not quite Centre City, which I would expect to be warmer due to the urban heat bubble effect.
I opted to exclude the Patco Line since the original dataset did not include it either. However a section of it does run through Centre City and could be relevant.
Credit for the piece goes to me, though the data is all from Billy Penn and the National Weather Service.
Those of you living on the East Coast, specifically the Mid-Atlantic, know that presently the weather is quite warm outside. As in levels of dangerous heat and humidity. Personally, your author has not left his flat in a few days now because it is so bad.
Alas, not everyone has access to air conditioning in his or her abode. Consequently, they need to look to public spaces with air conditioning. Usually that means libraries or public buildings. But here in Philadelphia, have people considered the subway?
Billy Penn investigated the temperatures in Philadelphia’s subsurface stations along the Broad Street and Market–Frankford Lines—Philadelphia’s third and oft-forgot line, the Patco, was untested. What they found is that temperatures in the stations were significantly below the temperatures above ground. The Market–Frankford stations, for example, were less than 100ºF.
Of course that misses the 2nd Street station in Old City, but otherwise picks up all the Market–Frankford stations situated underground.
Then there is the Broad Street Line.
Here, I do have a question about why the line wasn’t investigated from north to south. It ran only as far north as Girard, stopping well short of north Philadelphia neighbourhoods, and then as far south as Snyder, missing both Oregon and Pattison (sorry, corporately branded AT&T) stations. The robustness of the dataset is a bit worrying.
The colours here too mean nothing. Instead blue is used for the blue-coloured Market–Frankford line and orange for the orange-coloured Broad Street line. (The Patco line would have been red.) Here was a missed opportunity to encode temperature data along the route.
Finally, if the sidewalk temperatures were measured at each station, I would want to see that data alongside and perhaps run some comparisons.
This is an interesting story, but some more exploration and visualisation of the data could have taken it to the next level.
Last week the Philadelphia area experienced a mini tornado outbreak with three straight days of watches and warnings. Of course further west in the traditional Tornado Alley, far more storms of far greater intensity were wreaking havoc. But with tornado warnings going off every few minutes just outside the city of Philadelphia, it was hard to concentrate on storms in, say, Oklahoma.
But the New York Times did. And they put together a nice graphic showing the timeline of the outbreak using small multiples to show where the tornado reports were located on 12 consecutive days.
Of course the day of that publication, 29 May, would see another few dozen, even in and around Philadelphia. Consequently, the graphic could have been extended to a day 13. But that would have been rather unlucky.
From a design standpoint, the really nice element of this graphic is that it works so well in black and white. The graphic serves as a reminder that good graphics need not be super colourful and flashy to have impact.
Credit for the piece goes to Weiyi Cai and Jason Kao.
Today’s piece is another piece set against a black background. Today we look at one on natural disasters, created by both weather and geography/geology alike.
The Washington Post mapped a number of different disaster types: flooding, temperature, fire, lightning, earthquakes, &c. and plotted them geographically. Pretty clear patterns emerge pretty quickly. I was torn between which screenshots to share, but ultimately I decided on this one of temperature. (The earthquake and volcano graphic was a very near second.)
It isn’t complicated. Colder temperatures are in a cool blue and warmer temperatures in a warm red. The brighter the respective colour, the more intense the extreme temperatures. As you all know, I am averse to warm weather and so I will naturally default to living somewhere in the upper Midwest or maybe Maine. It is pretty clear that I will not really countenance moving to the desert southwest or Texas. But places such as Philadelphia, New York, and Washington are squarely in the blacked out or at least very dark grey range of, not super bad.
It’s cold out in Chicago. And not just the usual winter cold, but record-setting cold. And when the temperature gets that low, when you mix in a little bit of wind, it can become dangerous very quickly. In an article about the weather conditions in the Midwest, the BBC included this graphic at the end.
Even the slightest bit of wind decreases the time one has before frostbite sets in. So wrap up and stay warm, everyone.
Credit for the piece goes to the BBC graphics department.
Christmas time is a time when people receive gifts. Well this year was no different and I received a few. One, however, was in a box stuffed with old newspaper pages. And it turns out one of said pages had a graphic on it. So let us spend today looking at this little blast from the past.
The piece looks at PECO outages, PECO being the Philadelphia region’s main electricity supplier. The article is full page and is both headed and footed with photography, the graphic in which we are interested sits centre stage in the middle of the page.
Overall the graphic is fairly compact and works well at showing the distribution of the outages, which the bar chart below the choropleth shows was historically significant. (Despite my years in Chicago, I was somehow in the area for all but the storm written about and can confirm that they were, in fact, disruptive.)
The choropleth works, but I question the colour scheme. The bins diverge at about 50%, which to my knowledge marks no special boundary other than “half”. If that yellow bin represented, say, the average number of outages per storm or the acceptable number of outages per storm, sure, I could buy it. Otherwise, this is really just degrees of severity along one particular axis. I would have either kept the bins all red or all blue and proceeded from a light of either to a dark of either.
I probably would have also dropped Philadelphia entirely from the map, but I can understand how it may be important to geographically anchor readers in the most populous county to orientate themselves to a story about suburbia.
Lastly, I have one data question. With power lines down during an ice storm, I would be curious to see less of the important roadways as the map depicts and other variables. What about things like average temperature during the storm? Was the more urban and built-up Delaware County less susceptible because of an urban heat bubble preventing water from freezing? Or what about trees? Does the impact in the more rural areas have anything to do with increasing numbers of trees as one heads away from the city?
Those last data questions were definitely out of scope for the graphic, but I nevertheless remain curious. But then again, this piece is almost five years old. Just a look at how some graphical forms remain in use because of their solid ability to communicate data. Long live the bar chart. Long live the choropleth.
Credit for the piece goes to the Philadelphia Inquirer graphics department.
You may recall a few weeks ago there was a hurricane named Florence that slammed into the Carolina before stalling and dumping voluminous amounts of rain that inundated inland communities in addition to the damage by the storm surge in the coastal communities. At the time I wrote about a New York Times piece that explored housing density in coastal areas, specifically around the Florence impact area.
Well today the New York Times has a print graphic about something similar. It uses the same colours and styles, but swaps in a different data set and then uses a small multiple setup to include the Florida Panhandle. Of course the Florida Panhandle was just struck by Hurricane Michael, a Category 4 storm when it made landfall.
This one instead looks at median income per zip code to highlight the disparity between those living directly on the coast and those inland. In these two most recent landfall areas, the reader can clearly see that the zip codes along the coast have far greater incomes and, by proxy, wealth than those just a few zip codes further inland.
The problem is that rebuilding lives, communities, and infrastructure not only takes time, but also money. And with lower incomes, some of the hardest hit areas over the past several weeks could have a very difficult time recovering.
Regardless, the recoveries on the continental mainlands of the Carolinas and Florida will likely be far quicker and more comprehensive than they have been thus far for Puerto Rico.
The only downside with this graphic is the registration shift, which is why the graphic appears fuzzy as colours are ever so slightly offset whereas the single ink black text in the upper right looks clear and crisp.
Credit for the piece goes to the New York Times graphics department.
During my travels in Europe, I enjoyed very little sunshine. It did not rain often, but the skies were overcast in both Scandinavia and London. Turns out that at least in January, after my trip, Europe was covered in an inordinate amount of sunshine. The Guardian covered the story, with a graphic showing just how little sunshine has been seen in northern Europe.
Credit for the piece goes to the Guardian graphics department.
Over the last several weeks we dealt with the impact of a few hurricanes from H to K, i.e. Harvey, Irma, Jose, and Katia. Now that the Atlantic basin has quieted a wee bit, it is time we get back to the lighter side of things.
So we turn to xkcd and its look at ensemble models, often used to try and predict the paths of hurricanes.
Like many Americans I followed the story of Hurricane Irma over the weekend. One of my favourite pieces of reporting was this article from the Washington Post. It did a really nice job of visually comparing Irma to some recent and more historic storms, such as 1992’s Hurricane Andrew.
It can be difficult to truly compare hurricanes, sometimes they are small and compact, other times more dispersed. Irma was just big with lots of potentially destructive power spread out across a wide area, almost the width of the Floridian peninsula. The article uses several graphics—I am also quite partial to the satellite image comparison so check out the article—but this one is perhaps my favourite.
It uses a colour palette that deepens in redness nearer the storm’s centre. This allows the user to compare the geographic area or footprint of the storms destructive winds.
I wonder, however, what would happen if the designers had superimposed each graphic atop the other. It might have allowed for an even better comparison of size instead of having to have the user mentally transpose each hurricane.
Still, a really nice graphic and visual article.
Credit for the piece goes to Bonnie Berkowitz, Laris Karklis, Reuben Fischer-Baum, and Chiqui Esteban.